Methods for Segmentation and Classification of Digital Microscopy Tissue Images
Methods for Segmentation and Classification of Digital Microscopy Tissue Images
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DOI：
10.3389/fbioe.2019.00053
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发表时间：
2019-04-02
影响因子：
5.7
通讯作者：
Farahani, Keyvan
中科院分区：
文献类型：
--
作者：
Quoc Dang Vu;Graham, Simon;Farahani, Keyvan
组织标本的高分辨率显微镜图像提供有关正常和患病组织形态的详细信息。组织形态的图像分析可以帮助癌症研究人员更好地了解癌症生物学。细胞核分割和组织图像分类是组织图像分析中的两个常见任务。由于组织形态和肿瘤异质性的复杂性，为这些任务开发准确有效的算法是一个具有挑战性的问题。在本文中，我们提出了两种计算机算法；一个设计用于细胞核分割，另一个设计用于对整个切片组织图像进行分类。分割算法实现了多尺度深度残差聚合网络来精确分割核材料，然后将聚集的核分离成单独的核。该分类算法首先通过深度学习方法进行斑块级分类，然后将斑块级统计和形态特征作为随机森林回归模型的输入，以对整个幻灯片图像进行分类。分割和分类算法在 MICCAI 2017 数字病理学挑战赛中进行了评估。分割算法的准确度得分为 0.78。分类算法的准确率达到了 0.81。这些分数是挑战中的最高分数。
High-resolution microscopy images of tissue specimens provide detailed information about the morphology of normal and diseased tissue. Image analysis of tissue morphology can help cancer researchers develop a better understanding of cancer biology. Segmentation of nuclei and classification of tissue images are two common tasks in tissue image analysis. Development of accurate and efficient algorithms for these tasks is a challenging problem because of the complexity of tissue morphology and tumor heterogeneity. In this paper we present two computer algorithms; one designed for segmentation of nuclei and the other for classification of whole slide tissue images. The segmentation algorithm implements a multiscale deep residual aggregation network to accurately segment nuclear material and then separate clumped nuclei into individual nuclei. The classification algorithm initially carries out patch-level classification via a deep learning method, then patch-level statistical and morphological features are used as input to a random forest regression model for whole slide image classification. The segmentation and classification algorithms were evaluated in the MICCAI 2017 Digital Pathology challenge. The segmentation algorithm achieved an accuracy score of 0.78. The classification algorithm achieved an accuracy score of 0.81. These scores were the highest in the challenge.